Table of Contents

Contents

• 1. Machine Learning
  • 1.1 ML Theory: PAC Learnability
  • 1.2 ML Theory: VC Dimensions
  • 1.3 ML Theory: Linear Models
• 2. Deep Learning
• 3. Reinforcement Learning
  • 3.1 Multi Armed Bandits
    • 3.1.1 Multi Armed Bandits: Exploration vs Exploitation
    • 3.1.2 Stochastic Bandits: Explore The Commit
    • 3.1.3 Stochastic Bandits: The Epsilon Greedy Algorithm
    • 3.1.4 Stochastic Bandits: Optimism In The Face of Uncertainty
    • 3.1.5 Stochastic Bandits: Successive Elimination
    • 3.1.6 Stochastic Bandits: The MOSS Algorithm
    • 3.1.7 Adversarial Bandits: Hedge and Exp3
    • 3.1.8 Contextual Bandits: A Literature Review
    • 3.1.9 Bayesian Bandits: Conjugate Priors for Bernoulli and Gaussian Arms
    • 3.1.10 Bayesian Bandits: Thompson Sampling with Implementation and Regret Analysis
  • 3.2 Markov Decision Process
    • 3.2.1 MDPs: Markov Chains, Reducibility, Steady State Probabilities
    • 3.2.2 MDPs: Markov Reward Model, Markov Decision Process, Bellman Equations
    • 3.2.3 RL: Generalized Policy Iteration: Dynamic Programming vs Monte Carlo
• 4. Competitive Programming
  • 4.1 ACM ICPC Problems
    • 4.1.1 ACM ICPC Dhaka Regional 2008: Puzzles of Triangles
    • 4.1.2 ACM ICPC Dhaka Regional 2008: Stopping Doom's Day
• 5. Graph Theory
  • 5.1 Graph Theory: Matching
  • 5.2 Graph Theory: Hall's Marriage Theorem and It's Applications
• 6. Deep Reinforcement Learning
• 7. Inequalities
• 8. Olympiad Mathematics
  • 8.1 Geometry
    • 8.1.1 China Western Math Olympiad 2006: Multiple Solutions